




'''simple docstring'''



import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint




def   lowercase__(			__UpperCamelCase:					Any    ):

      """simple docstring"""






      return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()    )




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						{}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('heads.cmd.mim_head.cls.predictions'      ,'mmm_image_head'    )
            SCREAMING_SNAKE_CASE   :      int    					=						key.replace('heads.cmd.mlm_head.cls.predictions'      ,'mmm_text_head'    )
            SCREAMING_SNAKE_CASE   :      str    					=						key.replace('heads.cmd.itm_head.cls'      ,'itm_head'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('heads.cmd.itm_head.pooler'      ,'itm_head.pooler'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('heads.cmd.clip_head.logit_scale'      ,'flava.logit_scale'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('heads.fairseq_mlm.cls.predictions'      ,'mlm_head'    )
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						key.replace('heads.imagenet.mim_head.cls.predictions'      ,'mim_head'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						key.replace('mm_text_projection'      ,'flava.text_to_mm_projection'    )
            SCREAMING_SNAKE_CASE   :      List[str]    					=						key.replace('mm_image_projection'      ,'flava.image_to_mm_projection'    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						key.replace('image_encoder.module'      ,'flava.image_model'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('text_encoder.module'      ,'flava.text_model'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						key.replace('mm_encoder.module.encoder.cls_token'      ,'flava.multimodal_model.cls_token'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('mm_encoder.module'      ,'flava.multimodal_model'    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						key.replace('text_projection'      ,'flava.text_projection'    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						key.replace('image_projection'      ,'flava.image_projection'    )

            SCREAMING_SNAKE_CASE   :      Dict    					=						value.float()

      for key, value in codebook_state_dict.items():
            SCREAMING_SNAKE_CASE   :      int    					=						value

      return upgrade




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					str      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[int]=None    ):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      int    					=						FlavaConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						FlavaConfig()

      SCREAMING_SNAKE_CASE   :      str    					=						FlavaForPreTraining(__UpperCamelCase    ).eval()

      SCREAMING_SNAKE_CASE   :      int    					=						convert_dalle_checkpoint(__UpperCamelCase      ,__UpperCamelCase      ,save_checkpoint=__UpperCamelCase    )

      if os.path.exists(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      int    					=						torch.load(__UpperCamelCase      ,map_location='cpu'    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.hub.load_state_dict_from_url(__UpperCamelCase      ,map_location='cpu'    )

      SCREAMING_SNAKE_CASE   :      int    					=						upgrade_state_dict(__UpperCamelCase      ,__UpperCamelCase    )
      hf_model.load_state_dict(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						hf_model.state_dict()
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						count_parameters(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      int    					=						count_parameters(__UpperCamelCase    ) + count_parameters(__UpperCamelCase    )

      assert torch.allclose(__UpperCamelCase      ,__UpperCamelCase      ,atol=1e-3    )

      hf_model.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
      UpperCamelCase_    	=				argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
      parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      UpperCamelCase_    	=				parser.parse_args()

      convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		[
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)




